1. Field of the Invention
The field of the invention is data processing, or, more specifically, methods, apparatus, and products for analyzing network traffic using an improved Markov Modulated Poisson Process Model with two barrier states.
2. Description of Related Art
The flow of information in a network is often called ‘traffic.’ Units of information used in network communication are referred to as ‘packets.’ Packets generally arrive at a point in the network at random intervals resulting in ‘bursts’ of traffic, causing congestion, and resulting in ‘idle’ periods in which traffic is somewhat more sparse.
Systems that use a network for communication can benefit from the analysis and characterization of the network traffic to optimize the critical performance parameters for optimal utilization of the various network resources. Examples of applications for this type of analysis may include the synchronization of a user process with the completion of the receive operation at the system to network interface, load balancing, routing, quality of service management, and adaptive tuning of the system/network performance parameters. One way to perform the analysis of network traffic is to provide a model that recognizes the characteristics of the network traffic. Over the past decade, network traffic as been characterized as both bursty and self-similar in nature. Bursty behavior describes network traffic that arrives in bursts at a point in the network. Self-similarity is the phenomenon in which network traffic behaves the same when viewed at different degrees of magnification, or different scales on a time dimension. Because network traffic has been shown to be bursty and self-similar, a method used to analyze network traffic should be able to represent behavior that is bursty in self-similar traffic.
Different methods are known for analyzing and characterizing network traffic. The Poisson Process is a widely used model to analyze traffic from voice sources. Bursty behavior in self-similar traffic, however, has been shown to be approximated by a Markov Modulated Poisson Process (‘MMPP’). An MMPP model consists of two states that describe network traffic—a bursty state and an idle state. The bursty state represents the state of network traffic during a period when packet inter-arrival times are relatively small because the packets are arriving in bursts compared to other periods represented by the idle state in which packet inter-arrival times are relatively large because traffic is somewhat more sparse. The packet inter-arrival time is the time period between the arrival of one packet and the arrival of the next packet and may be measured from the perspective of one or more points on the network.
Current MMPP models work by transitioning between the bursty state and the idle state based on the mean inter-arrival times in each state and the inter-arrival time for the most recently received packet. The mean inter-arrival time for packets received in the bursty state is λBmean. The mean inter-arrival time for packets received in the idle state is λImean. The transition from the idle state to the bursty state occurs when the inter-arrival time of the most recently received packet drops below λBmean. Similarly, the transition from the bursty state to the idle state occurs when the inter-arrival time of the most recently received packet rises above λImean.
Although current MMPP models aid in the analysis of network traffic, current MMPP models often fail to detect premature or false transitions between the bursty and idle states. As a result, applications of network traffic analysis using current MMPP models suffer from inaccurate characterizations of network traffic. Those skilled in the art, therefore, appreciate that a need exists for improved models for analyzing network traffic.